1. Field of the Invention
The present invention relates to a meteorological information acquisition method, and more specifically, to a system which integrates meteorological information related to precipitation of a target region, particularly, classification of hydrometeors, estimation of specific differential phases by a distributive method, and a rainfall estimation method according thereto.
2. Background of the Related Art
Meteorological phenomena are closely related to life, and accurate forecast of the meteorological phenomena is a very important problem. A numerical forecasting system reflecting a variety of variables is required in order to improve the ability of forecasting the meteorological phenomena, and various techniques for such numerical forecasting are developed diversely.
The most important source of obtaining basic information for the numerical forecasting is radar data, and results of studies using the radar data, such as estimation of precipitation, estimation of a wind field, very short range forecast of precipitation and the like, help weather forecasters improve accuracy of forecast in real-time using the radar data and a computer.
Dual-polarimetric radar drawing attention recently as a weather forecasting system is very advantageous for improving accuracy of precipitation measurement, classifying hydrometeors and controlling data quality compared with existing single polarimetric radar, by the nature of observation. Accordingly, a lot of researches and developments related thereto are performed in order to utilize the advantages.
Typically, the dual-polarimetric radar transmits horizontally and vertically biased electromagnetic waves, receives back scattering signals, and obtains a lot of information related to meteorological phenomena by analyzing the signals.
Since a precipitation particle is not in an exact circular shape, horizontal and vertical backscatter cross sections are different. The electromagnetic waves propagating through rainfall induce scattering, differential attenuation, differential phase shift and depolarization. Such a signal continuously changes while the electromagnetic waves proceed and has information for estimating a size, a shape, a direction and a thermodynamic phase of a precipitation particle.
Accordingly, improvement of accuracy in rainfall estimation, classification of hydrometeors, and quality control according to classification of meteorological echo and non-meteorological echo can be accomplished through radar observation. Particularly, in the case of polarimetric radar, differential reflectivity Zdr, linear depolarization ratio (LDR), differential propagation phase φdp, cross correlation coefficient (ρhv) and the like, as well as reflectivity (Zh,v), Doppler velocity, and spectrum width observed by Doppler radar, can be observed, and studies on the types (properties) of objects (targets are mostly hydrometeors) and cloud physics can be conducted.
The most important thing among the meteorological information is precipitation. The precipitation has a great influence on life and works as one of most important information for coping with meteorological disasters such as heavy rain or heavy snow.
The precipitation is greatly affected by the type of hydrometeors or the size of hydrometeor particles. For example, a method of estimating precipitation for all types of hydrometeors regardless of the size thereof is not obtained yet, and since the types are very diverse, an equation itself for estimating precipitation based on the values observed through radar can be changed according to the type and size of a hydrometeor. Accordingly, for accurate estimation of precipitation, it is very important, first, to classify the type of hydrometeors and identify a precipitation type and, second, to accurately estimate the precipitation through the things closely related to the precipitation among the parameters obtained through observation.
First, observing the classification of hydrometeors, classification of radar observation targets will be more accurate if the types of hydrometeors are compared with each other, and although polarimetric observation variables for describing types of other hydrometeors are not clearly defined or overlapped, values of the observation variables are definitely different. If several hydrometeors are considered together, the hydrometeors are unclearly classified, and logic easily tends to be inaccurate. Therefore, a fuzzy logic method is used to classify the hydrometeors (Mandel, 1995).
A particle identification (PID) algorithm has been developed by Vivekanandan et al. in 1999, and a lot of studies on the PID algorithm in X-band dual-polarimetric radar are conducted recently. “Concentrative observation on dual-polarimetric radar in 2009: Classification of hydrometeors” presented by Mikyung Suk, Gyungyop Nam and Chunho Cho of National Institute of Meteorological Research in the Proceedings of 2009 Autumn Conference of Korea Meteorological Society disclosed a study on classification of particles into seventeen types based on seven input variables of horizontal reflectivity Zh, differential reflectivity Zdr, specific differential phase Kdp, cross correlation coefficient (ρhv), temperature (T), standard deviation of differential reflectivity, and standard deviation of differential phase.
Here, fuzzy logic is used to classify hydrometeors as described above. According to the PID algorithm using fuzzy logic, first, a membership function and a value thereof according to a precipitation particle or a hydrometeor are obtained for each dual-polarization (radar) variable (fuzzification). This membership function becomes a fuzzy function itself in most cases, and the fuzzy function is obtained through empirical or statistical data. A one-dimensional trapezoidal function or a one-dimensional beta function is mainly used in the classification of hydrometeors. In addition, the function has a value between 0 and 1.
In addition, a membership function exists according to each dual-polarimetric variable (a threshold value of the variable) and a precipitation type in the inference step, and a weighted sum (Q) of each precipitation type can be determined by adding a product of an interest value (P) and a weight (W) of each variable of the precipitation type for all variables.
A maximum value (Max(Q)) of the weighted sum is identified, and this maximum value is determined as a precipitation form of a corresponding particle (defuzzification).
The membership functions (function values) may be defined as an empirical value of conditional probability for a value of a special variable to which an input variable of each echo belongs. For example, in an example of classifying rainfall and hail using horizontal reflectivity Zh, differential reflectivity Zdr and a linear depolarization ratio (LDR) observed by radar as input values, a membership function value P for conditional precipitation classification can be determined as a value between 0 and 1 according to each hydrometeor type and input variable of six.
At the next step, a weighted value is determined for the membership function value determined above according to an observation value. For example, a weighted value of the horizontal reflectivity and the differential reflectivity may be respectively set to unit number 1, and a weighted value of the linear depolarization ratio may be set to 0.8.
Then, an aggregation value (Q) for classification of rainfall and hail is calculated by multiplying a membership function value by a weighted value of each of the three variables for the rainfall and adding the multiplied values of the three variables. Accordingly, the aggregation value can be obtained for hydrometeors of rainfall and hail through the rules and inference steps.
If the aggregation value of the rainfall is 1.7 and the aggregation value of the hail is 0.5, defuzzification of determining the hydrometeor in this region as the rainfall is performed.
The membership function, which is a function applied to a dual-polarimetric variable, is changed according to a threshold value of a variable, and determining the threshold value of the membership function is an important part of a study on the hydrometeor classification.
In addition, fuzzy logic of the fuzzification for calculating a specific function value by determining variables and membership functions and combining them has been disclosed in Classification of Hydrometeors Based on Polarimetric Radar Measurements: Development of Fuzzy Logic and Neuro-Fuzzy System, and In Situ Verification; JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY Volume 17, Hongping Liu and V. Chandrasekar, final form 27 Apr. 1999.
In addition, the present invention discloses a two-dimensional beta membership function (2D-MBF) configured in the form of multiplication of one-dimensional beta membership functions as shown below in equation 1.
                              beta          ⁡                      (                          x              ,              m              ,              a              ,              b                        )                          =                  1                      1            +                                          [                                                      (                                                                  x                        -                        m                                            a                                        )                                    2                                ]                            b                                                          [                  Equation          ⁢                                          ⁢          1                ]            
For example, if a one-dimensional membership function for rainfall with respect to horizontal polarization reflectivity Zh is frain-zh, it can be expressed as shown in equation 2.
                                          f                          rain              ⁢                                                          ⁢              _              ⁢                                                          ⁢              zh                                ⁡                      (                          Z              H                        )                          =                  1                      1            +                                          [                                                      (                                                                                            Z                          H                                                -                        42.5                                            18.17                                        )                                    2                                ]                            18.32                                                          [                  Equation          ⁢                                          ⁢          2                ]            
When Zh and Zdr are dealt as variables independent to each other with respect to the rainfall, the two-dimensional membership function in a two-dimensional plane of Zh−Zdr can be expressed through a graph shown in equation 3.ƒrain_zhzdr(ZHZDR)=ƒrain_zh(ZH)·ƒrain_zdr(ZDR)  [Equation 3]
Meanwhile, the differential propagation phase φdp and the cross correlation coefficient ρhv, which are dual-polarimetric variables or parameters, can be obtained using a series of equations shown below.
First, microphysical properties of the rain medium can be described by drop size distribution (DSD).
In order to study shapes of DSD at rainfall rates changing in a wide region, natural change of the DSD can be expressed by a standardized (normalized) gamma function as shown in the following equations.
                              N          ⁡                      (            D            )                          =                              N            w                    ⁢                      f            ⁡                          (              μ              )                                ⁢                                    (                              D                                  D                  0                                            )                        μ                    ⁢                      ⅇ                          -              AD                                                          [                  Equation          ⁢                                          ⁢          4                ]                                Λ        =                              3.67            +            μ                                D            0                                              [                  Equation          ⁢                                          ⁢          5                ]                                          f          ⁡                      (            μ            )                          =                              6                          3.67              4                                ⁢                                                    (                                  3.67                  +                  μ                                )                                            μ                +                4                                                    Γ              ⁡                              (                                  μ                  +                  4                                )                                                                        [                  Equation          ⁢                                          ⁢          6                ]            
At this point, D0 denotes an equivolumetric median volume diameter (unit is mm), μ denotes a shape parameter, NW denotes a normalized intercept parameter of exponential distribution having the same water content and D0.
Radar observation in the rain medium can be expressed from the aspect of the DSD, and reflectivity at horizontal and vertical polarization Zh,v can be defined as shown in the following equation.
                                          Z                          h              ,              v                                =                                                    λ                4                                                              π                  4                                ⁢                                                                                                K                      w                                                                            2                                                      ⁢                                          ∫                                  D                                      m                    ⁢                                                                                  ⁢                    i                    ⁢                                                                                  ⁢                    n                                                                    D                                      ma                    ⁢                                                                                  ⁢                    x                                                              ⁢                                                                    σ                                          h                      ,                      v                                                        ⁡                                      (                    D                    )                                                  ⁢                                  N                  ⁡                                      (                    D                    )                                                  ⁢                                                                  ⁢                                  ⅆ                  D                                                                    ;                  (                                    mm              6                        ⁢                          m                              -                3                                              )                                    [                  Equation          ⁢                                          ⁢          7                ]            
At this point, λ denotes a wavelength of radar, σh,v denotes a reflection cross section of radar at horizontal or vertical polarization, and Kw denotes a dielectric factor of water defined as Kw=(∈r−1)/(∈r+2) when ∈r is a complex dielectric factor of water.
The differential reflectivity Zdr is a ratio of reflectivity factors at horizontal and vertical polarization and defined as shown in the following equation.Zdr=10 log10(Zh/Zv)  [Equation 8]
In addition, the specific differential phase Kdp (unit of deg/km) can be defined as shown in the following equation.
                              K          dp                =                                            180              ⁢              λ                        π                    ⁢          R          ⁢                      ∫                                          [                                                                            f                      h                                        ⁡                                          (                      D                      )                                                        -                                                            f                      v                                        ⁡                                          (                      D                      )                                                                      ]                            ⁢                              N                ⁡                                  (                  D                  )                                            ⁢                              ⅆ                D                                                                        [                  Equation          ⁢                                          ⁢          9                ]            
At this point, R denotes the real part of a complex number, and fh and fv denote the size of forward scattering at horizontal and vertical polarization.
Real measurement values of dual-polarization rainfall radar are horizontal reflectivity Zh (mm6/m3), differential reflectivity Zdr (dB) and specific differential phase Kdp (deg/km).
The differential propagation phase φdp between target positions r1 and r2 is defined using the specific differential phase as shown in the following equation.φdp=s∫r1r2Kdp(r)dr  [Equation 10]
The cross correlation coefficient ρhv shows a correlation between signals at horizontal and vertical polarization and is defined through the equations shown below.ρhv=<svvshh*>/[<|shh|2>1/2<|svv|2>1/2]  [Equation 11]
                                                                  ρ              hv                        ⁡                          (              0              )                                                =                                                      〈                                                s                  vv                                ·                                  s                  hh                  *                                            〉                                                                        〈                                      s                    hh                    2                                    〉                                                  1                  /                  2                                            ·                                                〈                                      s                    vv                    2                                    〉                                                  1                  /                  2                                                                                                  [                  Equation          ⁢                                          ⁢          12                ]            
At this point, Shh and Svv are relation variables between electric fields horizontally received when microwaves are horizontally transmitted and electric fields vertically received when microwaves are vertically transmitted in a backscattering matrix (Zrnic, 1991), and the asterisk symbol is a complex conjugate which means a difference of 90° in the phase, and the bracket symbols mean expectation of an element. Magnitudes of these variables are determined by the radio frequency of the radar and the size, the shape and the configuration (state) of a hydrometeor particle.
The differential propagation phase is a difference between horizontal and vertical propagation phases, which is proportional to the forward scattering property of a hydrometeor. In the case of a horizontally biased hydrometeor such as a raindrop, horizontal propagation phase shift is larger than vertical propagation phase shift. In addition, in the case of a non-meteorological echo, variations of the differential propagation phase are definitely larger than the variations of precipitation due to a poor correlation.
The cross correlation coefficient is affected by the variation in the horizontal-to-vertical ratio of an individual hydrometeor. A value of the cross correlation coefficient approaches 1 in the case of rainfall or ice crystal. In the case of melting snow or a mixed state, the cross correlation coefficient is smaller than 1. A low value of the cross correlation coefficient can be used to detect hail or precipitation of a mixed state or to detect contamination caused by ground clutter and non-meteorological scattering.
Weather radar can be divided into an S-band, a C-band, an X-band and the like according to used frequencies. To use properly X-band radar data, especially reflectivity and differential reflectivity, an attenuation correction is very important since the attenuation phenomenon caused by precipitation is severe in higher frequencies such as X-band.
Since a conventional method uses an attenuation correction data chiefly using reflectivity even in classifying hydrometeors, a problem occurs according this.
In addition, although the conventional method classifies a region having a correlation coefficient remarkably lower than 1 as a melting layer using the correlation coefficient and classifies a hydrometeor below the melting layer as rainfall and a hydrometeor above the melting layer as snowfall in many cases, there is a problem in that since the correlation coefficient shows a different aspect according to the signal-to-noise ratio, if a melting layer is determined and a hydrometeor is classified by simply using a constant correlation coefficient value in a region of a low signal-to-noise ratio, it is difficult to correctly classify the hydrometeor, and thus estimation of precipitation is confused.
Second, observing the conventional precipitation estimation, a method of estimating precipitation is largely divided into a method of estimating the precipitation through a relation between reflectivity and rainfall intensity and a method of estimating the precipitation through a relation between a specific differential phase and rainfall intensity.
A lot of studies show that a specific differential phase Kdp accurately estimates rainfall in many cases. It is since that the specific differential phase is less sensitive to variations in the drop size distribution compared with an old method of using a relation between reflectivity and rainfall intensity (Z-R) (Sachidananda and Zrnic 1986; Matrosov et al. 2006; Cifelli and Chandrasekar 2010). Moreover, the specific differential phase is relatively less sensitive to hail.
Since X-band is more sensitive than S-band or C-band, using a specific differential phase may accomplish further better rainfall estimation at a high frequency compared with using the Z-R relation (Matrosov et al. 2002, 2006).
However, a disadvantage of the specific differential phase Kdp is that the specific differential phase is calculated across a path length, not at the gate of each range. Conventionally, the specific differential phase is calculated as a mean slope of range profiles of the differential propagation phase Ψdp(r) measured across a path and best fit across a specific path length. In order to estimate a specific differential phase from a measured differential propagation phase profile, Golestani et al. (1989) and Hubbert and Bringi (1995) used a filtering technique.
A general filtering method is a method of obtaining a specific differential phase by measuring changes of phase with respect to a unit distance (e.g., 1 km) after making a radio wave of a smooth shape overall by removing noises or the like from the radio wave by using a low pass filter several times by a user to be fit for the purpose, and this method works well in a rain region where microphysical properties do not abruptly change like stratiform rainfall. However, in a region of abrupt convective rainfall, a peak value of a specific differential phase is underestimated, and the specific differential phase even has a negative value. In addition, since the signal of the estimated specific differential phase changes greatly, it may vibrate severely even in a region showing a low rainfall rate (Wang and Chandrasekar 2009).
Wang and Chandrasekar (2009) have proposed an adaptive algorithm to reduce noises related to variations in a small segment and reduce deviation of estimation in a large segment. This method is configured to adjust regression errors through scaling for estimation of the specific differential phase. This method makes it possible to obtain a specific differential phase with an improved peak value even in a light rainfall region, as well as in a heavy rainfall region.
However, this method is based on a filtering method and thus inefficient for eliminating backscattering and solving the problem of negative specific differential phase values.